Datasets.
We train our model on ImageNet-100, comprising 100 randomly sampled ImageNet classes [1]}, COCO [2]} and Pascal VOC (PVOC) [3]}.
When fine-tuning on COCO-Stuff and COCO-Thing, we use a 10% split of the training sets.
Evaluation results are computed on the full COCO validation data for COCO-Stuff and COCO-Thing and PVOC12 val.
This setup up makes sure that our representations are assessed under varying object definitions (stuff vs. thing) and granularities.
Further details are provided in the Appendix.
